Radiometric Calibration of
Current and Future Ocean Color
Satellite Sensors
R. Foster, S. Hlaing, A. Gilerson, S. Ahmed
CoRP Science Symposium
September 9-10, 2014
Robert Foster, ME, LEED AP
Optical Remote Sensing Laboratory
The City College of New York
Outline
 Introduction & Motivation
 Datasets & Methodology
 Results
 Summary & Conclusion
Introduction & Motivation
This work is focused on coastal areas. Why?
 More than half of the world’s population exists in coastal regions.
 Harmful Algal Blooms cause 10% of all foodborne disease outbreaks
(toxins from algal blooms do not cook out of food).
 Economic activity associated with the ocean added $222 billion to the US
economy in 2009.
 Vacationers visiting coastal regions spend $44 billion annually.
 Much of the radiometric work associated with the open ocean has been
figured out already.
Facts taken from the Natural Resources Defense Council website
http://www.nrdc.org/water/oceans/ttw/health-economic.asp
Introduction & Motivation
Remote Sensing of Coastal Waters is extremely challenging.
Current Ocean Color (OC) sensors include MODIS-Aqua, and the
recently launched VIIRS.
Traditionally, OC sensors have relied on robust in-orbit radiometric
calibration and validation procedures based on the MOBY buoy.
The final adjustment made to the sensor’s TOA measurements, based
on surface measurements or climatology model, is known as a
vicarious calibration procedure.
Introduction & Motivation
There are two basic kinds of vicarious calibration.
The first one is usually referred to as the (system) vicarious
calibration procedure. In that procedure, calibration coefficients are
obtained by forcing satellite-derived water-leaving radiances to agree
with in-situ ones.
The second procedure, which is referred to as a radiometric
vicarious calibration, consists of simulating the TOA signal that the
sensor should measure under certain conditions, and to compare it to
the measured signal.
Datasets
 AERONET-OC
 Aerosol optical thickness (τaot)
 Single Scattering Albedo (SSA)
 Scattering phase functions
 Water Absorption and Scattering coefficients
(derived using a Quasi Analytical Algorithm)
 National Weather Service
 Atmospheric pressure
 Wind Speed
 NASA Ozone Monitoring Instrument (OMI)
 Molecular absorption in the atmosphere
 NASA MODIS-Aqua
 Top of Atmosphere Radiance Lt(λ)
 NASA VIIRS
 Top of Atmosphere Radiance Lt(λ)
Methodology
Satellite
Lt(λ)
 Simulated Top of Atmosphere (TOA) measurements are directly comparable to satellite
observations.
 This decouples the atmospheric correction procedure from the matchup – a major source of
uncertainty.
AERONET-OC sites
WaveCIS
LISCO
Time series of nLw(551nm), in mW/cm2/μm/sr
 WaveCIS
 particulate backscattering coefficient at 551 nm is usually around 0.01 m–1.
 seasonal average of the total absorption of water body is equal to 0.31 m–1 at 442 nm, of which
~0.15 m-1 is attributed to CDOM.
 LISCO
 the particulate backscattering coefficient at 551 nm ranges from 0.01 to 0.03 m–1,
 the total absorption coefficient at 443 nm varies from 0.38 to 1.2 m–1;
 the absorption due to CDOM at 443 nm is typically close to 0.4 m–1 and can be as high as 1 m–1.
 Three-year (2011–2013) average Angstrom exponent γ (443, 870) of LISCO is 1.76, whereas it is 1.23 for
WaveCIS. This implies that the aerosols over WaveCIS are dominated by coarse particles whereas the
LISCO site has notable contributions from fine aerosol particles.
Matchup comparisons between the
simulated and VIIRS Lt (λ)
WaveCIS
LISCO
 Excellent correlations with the overall R values close to 1 are observed for both sites.
 Spectral variation ranges of simulated and VIIRS Lt (λ) are the same.
 Regression lines for the comparisons are very close to 1:1 diagonals: simulated Lt (λ) data are spectrally
and magnitude wise consistent with those of measured (i.e. VIIRS).
Matchup comparisons between the simulated and VIIRS
Lt(λ) (Blue & Green parts of the spectrum)
 Excellent correlations with the
overall R values close to 1 are
observed at every wavelengths.
 Variation ranges of simulated and
VIIRS Lt are the same.
 Regression
lines
for
the
comparisons are very close to 1:1
diagonals.
 These observations underscore that
the simulated dataset is suitable for
making
assessments
of
the
radiometric accuracy and stability
of the Satellite Ocean Color sensors
in blue and green wavelengths.
Matchup comparisons between the simulated and VIIRS
Lt(λ) at each wavelength (Red & NIR parts of the spectrum)
 Different story emerges for Red and NIR wavelengths.
 Regression lines for the comparisons significantly deviate from
1:1 diagonals.
 Simulated data underestimate ~10% & ~30% respectively at
671 and 862 nm wavelengths.
 Excellent correlations are also observed at both wavelengths.
 This observed discrepancy between the simulated and VIIRS
data will be further scrutinized in next few slides.
Matchup comparisons between the
simulated and MODIS Lt (λ) at
WaveCIS
 Unlike VIIRS, the uncertainties between MODIS and
simulated Lt are higher at every wavelength.
 Only modest correlation is achieved for the comparisons
at 551, 667 & 869 nm (R values are 0.85, 0.78 & 0.75
respectively).
 This can be largely explained by the MODIS’s lower 1
km nadir nominal resolution
Derivation of the radiometric vicarious
calibration gain factors
VIIRS Vicarious Calibration Gains
1.1
1
0.9
Current
WaveCIS
LISCO
0.8
0.7
0.6
0.5
410
443
486
551
Wavelength
671
745
862
 The gc values derived for
both VIIRS MODIS for
blue and green wavelengths
are within typical vicarious
adjustment range.
 Such large deviations also
exist for MODIS in NIR
channels.
MODIS Vicarious Calibration Gains
1.1
1
0.9
0.8
Current
0.7
WaveCIS
0.6
0.5
412
443
488
547
Wavelength
667
748
869
g c ( ) 
1
N
N

i 1
Li ( )
Sim
Li ( )
Sat
simulated
satellite
Similar Results by a Third Party
Sensor
SeaWiFS
MODIS
MODIS
VIIRS
VIIRS
Site
AAOT*
AAOT*
WaveCIS
WaveCIS
LISCO
0.9693
0.8917
0.9123
671(667) nm
channels
0.90546 0.9154
745 (748) nm
channels
0.851
0.875
0.8444
0.854
0.868
862 (869) nm
channels
0.83
0.839
0.834
0.706
0.700
 In the vicarious calibration study by Melin and Zibordi* for MODIS and SeaWiFS
sensors based on the data from the AAOT AERONET-OC site but with a different
methodology, similar trend is observed in Red and NIR.
 Although correlations between the simulated and satellite TOA Lt at the red and NIR
channels are high (R ≥ 0.93 for those channels), validities of the resulting gc values are
inconclusive at the moment, further separate study should be granted to resolve the
issues.
*F. Mélin and G. Zibordi, "Vicarious calibration of satellite ocean color sensors at two coastal sites," Appl. Opt. 49, 798-810 (2010).
Summary
 Coastal locations of AERONET-OC site make this particularly suited for improving coastal satellite
retrievals.
 Cross-site uncertainty is well below or around the 0.5% in blue and green part of the spectrum.
 Including more AERONET sites in the gain derivation process can help reduce the overall uncertainty.
 The derivations are intended only as capability demonstration and should not be regarded as the final
results.
 It can be expected that in an ideal case, as long as the sensor calibration improves, both the MOBY
based system vicarious approach and this RT based approach should converge providing high quality
atmospheric and oceanic data in open ocean and coastal areas with the current or slightly adjusted
atmospheric correction.
For More Information:
S. Hlaing, A. Gilerson, R. Foster, M. Wang, R. Arnone, S. Ahmed, “A Radiometric Approach for
Calibration of Current and Future Ocean Color Satellite Sensors ,” Optics Express. Currently in press.
Thank you!
Appendix
Satellite Data Filtering and
Processing Procedures
 The VIIRS and MODIS TOA Lt (λ) used for comparisons with the simulation results are all extracted
from a small region (3×3 pixel box) centered at the site locations.
 Average value of the Lt (λ) of 3×3 pixel box, except the center one, is evaluated against simulated TOA
Lt (λ). The exclusion of the central pixel is intended to minimize the potential uncertainty resulting from
the platform effects due to the high albedo of the platform structure.
 We also exclude the satellite Lt (λ) data with high spatial variability from the analysis, using the relative
standard deviation (coefficient of variation), calculated as σrel = σ/µ where σ and µ are the standard
deviation and mean, respectively. σrel is set to 0.2.
 Level 2 quality flag conditions are acquired through the standard NASA processing scheme: land,
cloud, stray light, bad navigation quality, both high and moderate glint, and high sensor viewing and/or
solar zenith angles.
 Data points with the sensor–sun relative azimuth angle less than 40° (i.e., TOA radiance measurements
made close to the direct solar path) are also excluded from the analysis.
 In addition, at least 50% of the pixels in the region of interest must satisfy all quality flag conditions in
order to qualify for the match-up comparisons with the simulated TOA Lt (λ) and to be used in the
derivation of radiometric vicarious gain factors.
Statistical Filtering Procedure
A statistical match-up comparison filtering procedure is further applied.
relative percentage difference of the ith match-up RPD
i
 200 % 
 yi  xi 
 y  xi 
 xi and yi stand for the ith individual satellite and simulated match-up TOA radiance data
points, respectively.
 Then the initial average (μRPD) and standard deviation (σRPD) of the all resulting RPDi
between the two data being compared are calculated.
 After that any match-ups with the RPDi values outside the μRPD ± ∆ σRPD range are excluded
from further analysis.
 This procedure is applied just to ensure that the values of the statistical parameters thus
obtained are not skewed by one or a very few extreme cases whose statistics are entirely out
of range of the majority of cases.
 ∆ is set to 2 for all matchup comparison analysis and 1 for the radiometric vicarious gain
derivation.
 In-situ data
In-situ & Satellite Data
 All input AERONET-OC data used in this study are level 1.5 data.
 AERONET-OC data to the RT simulations are selected from the measurements made within a ±2h time
window of the satellite overpass time of the locations of the sites.
 SeaPRISM’s center wavelengths (413, 442, 491, 551, 667 & 870 nm) are slightly different from those of
VIIRS’s (410, 443, 486, 551, 671 & 862). Therefore aerosol optical thickness data (τaot) at exact VIIRS
center wavelengths are obtained by interpolation using Angstrom exponent (γ) at corresponding
wavelengths.
 Satellite data
 NASA VIIRS data
 Pseudo Level 1 VIIRS images of the LISCO and WaveCIS locations have been obtained for two years
(January 2012 to December 2013) period from NASA OBPG ocean color website.
 Then, SeaDAS software version 7.0.2 package in order to generate the fully calibrated and geo-located level 2
data.
 IDPS VIIRS data
 VIIRS SDR data of IDPS processing is obtained from NOAA NESDIS. They are completely calibrated and
geo-located at the SDR level & do not require additional processing.
 MODIS data
 MODIS level 1 collection 6 images are acquired from the NASA Level 1 and Atmosphere Distribution System
(LAADS). These data are also fully calibrated and geo-located.
 Note: Comparison between the NASA and IDPS VIIRS Lt data exhibits almost perfect correlation at every wavelength but
slightly time dependent discrepancy (1 – 3%) in terms of magnitude. For consistency purpose, we used only the NASA VIIRS
data for all analyses in this study.